A deep learning framework based on Koopman operator for data-driven modeling of vehicle dynamics. - Yongqian Xiao; Xinglong Zhang; Xin Xu 001; Xueqing Liu; Jiahang Liu (CoRR, 2020)

CKNet: A Convolutional Neural Network Based on Koopman Operator for Modeling Latent Dynamics from Pixels. - Yongqian Xiao; Xin Xu 001; Qianli Lin (CoRR, 2021)

DDK: A Deep Koopman Approach for Dynamics Modeling and Trajectory Tracking of Autonomous Vehicles. - Yongqian Xiao (CoRR, 2021)

DDK: A Deep Koopman Approach for Longitudinal and Lateral Control of Autonomous Ground Vehicles. - Yongqian Xiao; Xinglong Zhang; Xin Xu 001; Yang Lu; Junxiang Li (ICRA, 2023)

Deep Neural Networks With Koopman Operators for Modeling and Control of Autonomous Vehicles. - Yongqian Xiao; Xinglong Zhang; Xin Xu 001; Xueqing Liu; Jiahang Liu (IEEE Trans. Intell. Veh., 2023)

Vector Field-Guided Learning Predictive Control for Motion Planning of Mobile Robots with Unknown Dynamics. - Yang Lu; Weijia Yao; Yongqian Xiao; Xin Xu 001 (CoRR, 2024)

A deep Koopman operator-based modelling approach for long-term prediction of dynamics with pixel-level measurements. - Yongqian Xiao; Zixin Tang; Xin Xu 001; Xinglong Zhang; Yifei Shi (CAAI Trans. Intell. Technol., 2024)

AMARL: An Attention-Based Multiagent Reinforcement Learning Approach to the Min-Max Multiple Traveling Salesmen Problem. - Hao Gao 014; Xing Zhou 004; Xin Xu 001; Yixing Lan; Yongqian Xiao (IEEE Trans. Neural Networks Learn. Syst., 2024)